Zinnia Mondal
Application of Corporate Governance indices to predict Bankruptcy of Companies using Machine Learning.
Rel. Guido Perboli, Filippo Velardocchia. Politecnico di Torino, Master of science program in Communications And Computer Networks Engineering, 2022
|
Preview |
PDF (Tesi_di_laurea)
- Thesis
Licence: Creative Commons Attribution Non-commercial No Derivatives. Download (2MB) | Preview |
Abstract
Bankruptcy Prediction of companies is becoming increasingly significant in recent times as it enables stakeholders to act quickly and reduce their financial losses. To create bankruptcy prediction models, many machine learning techniques have been applied using financial features. However, there have been comparatively less research on how non-financial features like Corporate Governance indices can be used to predict a company’s performance. Hence, this thesis is motivated by the need of further research within bankruptcy prediction influenced by Governance indices using traditional machine learning models and neural networks as there has been very less research using only the governance indices as the contributing features.
These governance indices can be for example the age of the company, the number of shareholders, the number of board members etc of a company
Relators
Academic year
Publication type
Number of Pages
Course of studies
Classe di laurea
Aziende collaboratrici
URI
![]() |
Modify record (reserved for operators) |
